Copyright: ©Author(s) 2026. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license. No commercial re-use. See permissions. Published by Baishideng Publishing Group Inc.
World J Clin Oncol. Apr 24, 2026; 17(4): 117540
Published online Apr 24, 2026. doi: 10.5306/wjco.v17.i4.117540
Published online Apr 24, 2026. doi: 10.5306/wjco.v17.i4.117540
Precision therapy for driver gene mutations in breast cancer: Current landscape and future perspectives
Zhi-Yong Liu, Ran Chen, Breast Diagnosis and Treatment Center, First Affiliated Hospital of Gannan Medical University, Ganzhou 341000, Jiangxi Province, China
Author contributions: Liu ZY designed the article format, wrote the manuscript, and revised the original draft; Chen R reviewed and searched the literature; and all authors studied and approved the final version of this manuscript.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Corresponding author: Zhi-Yong Liu, MD, Breast Diagnosis and Treatment Center, First Affiliated Hospital of Gannan Medical University, No. 128 Jinling Road, Huangjin Technology Development Zone, Ganzhou 341000, Jiangxi Province, China. barton123321@163.com
Received: December 10, 2025
Revised: January 6, 2026
Accepted: February 10, 2026
Published online: April 24, 2026
Processing time: 132 Days and 23.7 Hours
Revised: January 6, 2026
Accepted: February 10, 2026
Published online: April 24, 2026
Processing time: 132 Days and 23.7 Hours
Core Tip
Core Tip: This article delineates the paradigm of driver gene mutation-guided precision therapy in breast cancer. We synthesize the clinical application of targeting key mutations (e.g., PIK3CA, ESR1, BRCA1/2) and analyze the complex landscape of acquired resistance. Critically, we propose a forward-looking framework integrating multi-omics, liquid biopsy, and artificial intelligence to dynamically navigate treatment. The article underscores the necessity of evolving from static biomarker testing towards a proactive, digitally-enabled ecosystem to overcome resistance and optimize sequential therapy, ultimately aiming for sustained patient benefit.